diff --git a/aio.html b/aio.html index f8325a9..9aa787d 100644 --- a/aio.html +++ b/aio.html @@ -368,7 +368,7 @@
Content from Introduction to Bioconductor and the SingleCellExperiment class
Last updated on 2024-10-04 | +
Last updated on 2024-10-07 | Edit this page
Let’s look at an example dataset. WTChimeraData
comes
-from a study on mouse development. We can assign one sample to a
-SingleCellExperiment
object named sce
like
-so:
We can assign one sample to a SingleCellExperiment
+object named sce
like so:
Content from Exploratory data analysis and quality control
+ -->Content from Exploratory data analysis and quality control
Last updated on 2024-10-03 | @@ -2744,7 +2756,7 @@
We see in the help documentation for ?clusterCells
that
all of the clustering algorithm details are handled through the
@@ -2941,7 +2953,7 @@
You can see that at least among the top markers, cluster 6 (pale green) tends to have the least separation from cluster 1.
@@ -3239,7 +3251,7 @@The NNGraphParam
constructor has an argument
cluster.args
. This allows to specify arguments passed on to
@@ -3670,7 +3682,7 @@
One important reason why is because averages over all other clusters can be sensitive to the cell type composition. If a rare cell type shows @@ -3816,7 +3828,7 @@
Use BiocParallel
and the BPPARAM
argument!
This example will set it to use four cores on your laptop, but you can
@@ -3858,7 +3870,7 @@
The example that jumps out most strongly to the eye is ExE endoderm, which doesn’t show clear separate modes. Simultaneously, Endothelium @@ -4151,7 +4163,7 @@
Samples 5 and 6 were from the same “pool” of cells. Looking at the
documentation for the dataset under ?WTChimeraData
we see
@@ -4216,7 +4228,7 @@
False. Batch-level data can be retained through confounding with experimental factors or poor ability to distinguish experimental effects @@ -4669,7 +4681,7 @@
“logFC” stands for log fold-change. edgeR
uses a log2
convention. Rather than reporting e.g. a 5-fold increase, it’s better to
@@ -4995,7 +5007,7 @@
You can simply hand pheatmap()
a matrix as its only
argument. pheatmap()
has a million options you can tweak,
@@ -5009,7 +5021,7 @@
After running the second pseudobulk DGE, you can join the two
DataFrame
s of Erythroid3
statistics using the
@@ -5059,7 +5071,7 @@
It’s important to have multiple samples within each experimental group because it helps the batch effect correction algorithm distinguish @@ -5219,7 +5231,7 @@
Content from Working with large data
Last updated on 2024-10-04 | +
Last updated on 2024-10-07 | Edit this page
From ?MulticoreParam
:
@@ -5710,21 +5722,22 @@ROUTPUT
+exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 + 1 88 0 0 0 0 0 0 2 0 0 0 0 0 0 + 2 0 86 0 0 0 0 0 0 0 0 0 0 0 0 + 3 0 57 75 1 0 0 0 0 0 0 0 1 0 0 + 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 + 5 0 0 0 0 176 0 0 0 0 0 0 11 0 0 + 6 0 0 0 0 0 73 128 0 0 1 0 0 0 0 + 7 0 0 0 0 0 253 0 0 1 0 0 0 0 0 + 8 1 0 0 0 0 0 0 106 0 0 0 1 0 0 + 9 0 0 0 0 0 0 0 0 113 0 10 0 0 0 + 10 0 0 0 0 0 0 0 0 0 153 0 0 0 0 + 11 0 0 0 0 0 0 0 0 0 0 198 0 0 0 + 12 0 0 0 0 0 0 0 0 1 0 0 312 0 0 + 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 + 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 + 15 0 0 0 56 0 0 0 0 0 0 0 0 0 0approx -exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 - 1 90 0 0 0 1 0 0 0 1 0 0 0 0 0 0 - 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1 - 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0 - 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 56 - 5 0 0 0 0 392 0 0 1 0 1 0 0 0 0 0 - 6 0 0 0 0 0 79 131 0 0 0 0 0 0 0 0 - 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0 - 8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0 - 9 1 0 0 0 2 0 0 0 106 0 0 0 0 0 0 - 10 0 0 0 0 0 0 0 0 0 105 0 0 0 0 0 - 11 0 0 0 0 0 0 1 0 0 5 147 0 0 0 0 - 12 0 0 0 0 1 0 0 0 0 23 0 199 0 0 0 - 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0 - 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
The similarity of the two clusterings can be quantified by calculating the pairwise Rand index:
@@ -5869,7 +5882,7 @@This code block calculates the exact PCA coordinates. Another thing to note: PC vectors are only identified up to a sign flip. We can see @@ -6247,7 +6260,7 @@
See the HDF5Array
function for reading from HDF5 and the
writeHDF5Array
function for writing to HDF5 from the HDF5Array
@@ -6294,7 +6307,7 @@
Use the function system.time
to obtain the runtime of
each job.
Content from Accessing data from the Human Cell Atlas (HCA)
+ --> +Content from Accessing data from the Human Cell Atlas (HCA)
Last updated on 2024-10-04 | @@ -6931,7 +6942,7 @@
We see in the help documentation for ?clusterCells
that
all of the clustering algorithm details are handled through the
@@ -710,7 +710,7 @@
You can see that at least among the top markers, cluster 6 (pale green) tends to have the least separation from cluster 1.
@@ -1005,7 +1005,7 @@The NNGraphParam
constructor has an argument
cluster.args
. This allows to specify arguments passed on to
@@ -1430,7 +1430,7 @@
One important reason why is because averages over all other clusters can be sensitive to the cell type composition. If a rare cell type shows @@ -1575,7 +1575,7 @@
Use BiocParallel
and the BPPARAM
argument!
This example will set it to use four cores on your laptop, but you can
@@ -1617,7 +1617,7 @@
The example that jumps out most strongly to the eye is ExE endoderm, which doesn’t show clear separate modes. Simultaneously, Endothelium diff --git a/hca.html b/hca.html index 37dfb6a..60cb06d 100644 --- a/hca.html +++ b/hca.html @@ -758,7 +758,7 @@
Content from Introduction to Bioconductor and the SingleCellExperiment class
Last updated on 2024-10-04 | +
Last updated on 2024-10-07 | Edit this page
Estimated time: 30 minutes
@@ -573,9 +573,14 @@Let’s look at an example dataset. WTChimeraData
comes
-from a study on mouse development. We can assign one sample to a
-SingleCellExperiment
object named sce
like
-so:
We can assign one sample to a SingleCellExperiment
+object named sce
like so:
Content from Exploratory data analysis and quality control
+ -->Content from Exploratory data analysis and quality control
Last updated on 2024-10-03 | @@ -2749,7 +2761,7 @@
We see in the help documentation for ?clusterCells
that
all of the clustering algorithm details are handled through the
@@ -2946,7 +2958,7 @@
You can see that at least among the top markers, cluster 6 (pale green) tends to have the least separation from cluster 1.
@@ -3244,7 +3256,7 @@The NNGraphParam
constructor has an argument
cluster.args
. This allows to specify arguments passed on to
@@ -3675,7 +3687,7 @@
One important reason why is because averages over all other clusters can be sensitive to the cell type composition. If a rare cell type shows @@ -3821,7 +3833,7 @@
Use BiocParallel
and the BPPARAM
argument!
This example will set it to use four cores on your laptop, but you can
@@ -3863,7 +3875,7 @@
The example that jumps out most strongly to the eye is ExE endoderm, which doesn’t show clear separate modes. Simultaneously, Endothelium @@ -4157,7 +4169,7 @@
Samples 5 and 6 were from the same “pool” of cells. Looking at the
documentation for the dataset under ?WTChimeraData
we see
@@ -4222,7 +4234,7 @@
False. Batch-level data can be retained through confounding with experimental factors or poor ability to distinguish experimental effects @@ -4675,7 +4687,7 @@
“logFC” stands for log fold-change. edgeR
uses a log2
convention. Rather than reporting e.g. a 5-fold increase, it’s better to
@@ -5001,7 +5013,7 @@
You can simply hand pheatmap()
a matrix as its only
argument. pheatmap()
has a million options you can tweak,
@@ -5015,7 +5027,7 @@
After running the second pseudobulk DGE, you can join the two
DataFrame
s of Erythroid3
statistics using the
@@ -5065,7 +5077,7 @@
It’s important to have multiple samples within each experimental group because it helps the batch effect correction algorithm distinguish @@ -5225,7 +5237,7 @@
Content from Working with large data
Last updated on 2024-10-04 | +
Last updated on 2024-10-07 | Edit this page
Estimated time: 12 minutes
@@ -5644,7 +5656,7 @@From ?MulticoreParam
:
@@ -5717,21 +5729,22 @@ROUTPUT
+exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 + 1 88 0 0 0 0 0 0 2 0 0 0 0 0 0 + 2 0 86 0 0 0 0 0 0 0 0 0 0 0 0 + 3 0 57 75 1 0 0 0 0 0 0 0 1 0 0 + 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 + 5 0 0 0 0 176 0 0 0 0 0 0 11 0 0 + 6 0 0 0 0 0 73 128 0 0 1 0 0 0 0 + 7 0 0 0 0 0 253 0 0 1 0 0 0 0 0 + 8 1 0 0 0 0 0 0 106 0 0 0 1 0 0 + 9 0 0 0 0 0 0 0 0 113 0 10 0 0 0 + 10 0 0 0 0 0 0 0 0 0 153 0 0 0 0 + 11 0 0 0 0 0 0 0 0 0 0 198 0 0 0 + 12 0 0 0 0 0 0 0 0 1 0 0 312 0 0 + 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 + 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 + 15 0 0 0 56 0 0 0 0 0 0 0 0 0 0approx -exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 - 1 90 0 0 0 1 0 0 0 1 0 0 0 0 0 0 - 2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1 - 3 0 0 75 0 2 0 0 0 0 0 0 0 0 0 0 - 4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 56 - 5 0 0 0 0 392 0 0 1 0 1 0 0 0 0 0 - 6 0 0 0 0 0 79 131 0 0 0 0 0 0 0 0 - 7 0 0 0 0 0 245 0 0 0 1 0 0 0 0 0 - 8 0 0 0 0 0 0 0 95 0 0 0 0 0 0 0 - 9 1 0 0 0 2 0 0 0 106 0 0 0 0 0 0 - 10 0 0 0 0 0 0 0 0 0 105 0 0 0 0 0 - 11 0 0 0 0 0 0 1 0 0 5 147 0 0 0 0 - 12 0 0 0 0 1 0 0 0 0 23 0 199 0 0 0 - 13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0 - 14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
The similarity of the two clusterings can be quantified by calculating the pairwise Rand index:
@@ -5876,7 +5889,7 @@This code block calculates the exact PCA coordinates. Another thing to note: PC vectors are only identified up to a sign flip. We can see @@ -6254,7 +6267,7 @@
See the HDF5Array
function for reading from HDF5 and the
writeHDF5Array
function for writing to HDF5 from the HDF5Array
@@ -6301,7 +6314,7 @@
Use the function system.time
to obtain the runtime of
each job.